Sequential Designs for Filling Output Spaces

Abstract

Space-filling designs are commonly used in computer experiments to fill the space of inputs so that the input-output relationship can be accurately estimated. However, in certain applications such as inverse design or feature-based modeling, the aim is to fill the response or feature space. In this article, we propose a new experimental design framework that aims to fill the space of the outputs (responses or features). The design is adaptive and model-free, and therefore is expected to be robust to different kinds of modeling choices and input-output relationships. Several examples are given to show the advantages of the proposed method over the traditional input space-filling designs.

arXiv comment36 pages, 12 figures

References in corpus (6)

• Outlook for inverse design in nanophotonics
• An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
• Adaptive Exploration and Optimization of Materials Crystal Structures
• Sequential Designs for Filling Output Spaces
• A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling
• Space-filling design for nonlinear models

Cited by in corpus (1)

• Sequential Designs for Filling Output Spaces